## Filter for Specific level
# ggo <- groupGO(gene = rownames(tab_sig),
# OrgDb = org.Hs.eg.db,
# ont = "BP",
# level = 1,
# keyType = "UNIPROT",
#
# readable = TRUE)
# universe<-ggo@result[ggo@result$Count>0,]$geneID
# universe<-strsplit(universe,"/")
# universe<-unlist(universe)
# universe<-unique(universe)### GO with specific level (PROVISIONAL)
# GO_ORA_level<-enrichGO(
# rownames(tab_sig),
# OrgDb=org.Hs.eg.db,
# keyType = "UNIPROT",
# ont = "BP",
# pvalueCutoff = 0.05,
# pAdjustMethod = "BH",
# universe=universe,
# qvalueCutoff = 0.2,
# minGSSize = 1,
# maxGSSize = 5000,
# readable = FALSE
# )# Without universe
GO_ORA<-enrichGO(
rownames(tab_sig),
OrgDb=org.Hs.eg.db,
keyType = "UNIPROT",
ont = "BP",
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
universe=rownames(tab),
qvalueCutoff = 0.2,
# minGSSize = 1,
# maxGSSize = 5000,
readable = FALSE
)
library(rrvgo)
GO_ORA<-
GO_ORA@result %>% filter(p.adjust<=0.05)
if(dim(GO_ORA)[1]>20){
simMatrix <- calculateSimMatrix(GO_ORA$ID ,
orgdb="org.Hs.eg.db",
ont="BP",
method="Rel")
scores <- setNames(-log10(GO_ORA$qvalue), GO_ORA$ID)
reducedTerms <- reduceSimMatrix(simMatrix,
scores,
threshold=0.7,
orgdb="org.Hs.eg.db")
treemapPlot(reducedTerms)
GO_ORA<-merge(GO_ORA,reducedTerms,by.x="ID",by.y="go")
datatable_jm(GO_ORA,column = c("geneID","term"))
}else{
datatable_jm(GO_ORA,column = c("geneID"))
}With universe there are not any term significative
# Without universe
GO_ORA<-enrichGO(
rownames(tab_sig),
OrgDb=org.Hs.eg.db,
keyType = "UNIPROT",
ont = "BP",
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
# universe=rownames(tab),
qvalueCutoff = 0.2,
# minGSSize = 1,
# maxGSSize = 5000,
readable = T
)
library(rrvgo)
GO_ORA<-
GO_ORA@result %>% filter(p.adjust<=0.05)
if(dim(GO_ORA)[1]>20){
simMatrix <- calculateSimMatrix(GO_ORA$ID ,
orgdb="org.Hs.eg.db",
ont="BP",
method="Rel")
scores <- setNames(-log10(GO_ORA$qvalue), GO_ORA$ID)
reducedTerms <- reduceSimMatrix(simMatrix,
scores,
threshold=0.7,
orgdb="org.Hs.eg.db")
treemapPlot(reducedTerms)
GO_ORA<-merge(GO_ORA,reducedTerms,by.x="ID",by.y="go")
datatable_jm(GO_ORA,column = c("geneID","term"))
}else{
datatable_jm(GO_ORA,column = c("geneID"))
}# geneList<-tab$logFC
# names(geneList)<-rownames(tab)
# geneList<-sort(geneList,decreasing = T)
# GO_GSEA <- gseGO(geneList = geneList,
# OrgDb = org.Hs.eg.db,
# ont = "BP",
# keyType = "UNIPROT",
#
# # minGSSize = 100,
# # maxGSSize = 500,
# pvalueCutoff = 0.05,
# # readable=T,
# verbose = FALSE)
#
geneList<-tab$logFC
names(geneList)<-tab$symbol
geneList<-sort(geneList,decreasing = T)
geneList<-geneList[!is.na(names(geneList))]
GO_GSEA <- gseGO(geneList = geneList,
OrgDb = org.Hs.eg.db,
ont = "BP",
keyType = "SYMBOL",
# minGSSize = 100,
# maxGSSize = 500,
pvalueCutoff = 0.05,
# readable=T,
verbose = FALSE)
library(rrvgo)
GO_GSEA<-GO_GSEA@result %>% filter(p.adjust<=0.05)
if(dim(GO_GSEA)[1]>20){
simMatrix <- calculateSimMatrix(GO_GSEA$ID ,
orgdb="org.Hs.eg.db",
ont="BP",
method="Rel")
scores <- setNames(-log10(GO_GSEA$qvalue), GO_GSEA$ID)
reducedTerms <- reduceSimMatrix(simMatrix,
scores,
threshold=0.7,
orgdb="org.Hs.eg.db")
treemapPlot(reducedTerms)
GO_GSEA<-merge(GO_GSEA,reducedTerms,by.x="ID",by.y="go")
datatable_jm(GO_GSEA,column = c("core_enrichment","leading_edge","term"))
}else{
datatable_jm(GO_GSEA,column = c("core_enrichment","leading_edge"))
}kegg<-enrichKEGG(tab_sig$entrez,
organism = "hsa",
universe = tab$entrez,
# keyType = "entrez",
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
qvalueCutoff = 0.2,
use_internal_data = FALSE)
kegg<-setReadable(kegg,OrgDb = org.Hs.eg.db,keyType = "ENTREZID")
kegg_sig<-
kegg@result %>%
filter(p.adjust<=0.05)
cat("### Table with gens splited")kegg<-enrichKEGG(tab_sig$entrez,
organism = "hsa",
# universe = rownames(tab),
# keyType = "uniprot",
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
qvalueCutoff = 0.2,
use_internal_data = FALSE)
kegg<-setReadable(kegg,OrgDb = org.Hs.eg.db,keyType = "ENTREZID")
kegg_sig<-
kegg@result %>%
filter(p.adjust<=0.05)
datatable_jm(kegg_sig,"geneID")geneList<-tab$logFC
names(geneList)<-tab$entrez
geneList<-sort(geneList,decreasing = T)
geneList<-geneList[!is.na(names(geneList))]
kegg_gsea <- gseKEGG(geneList = geneList,
organism = "hsa",
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
keyType = "ncbi-geneid"
)
kegg_gsea<-setReadable(kegg_gsea,OrgDb = org.Hs.eg.db,keyType = "ENTREZID")
kegg_gsea_sig<- kegg_gsea@result %>%
filter(p.adjust<0.05)
datatable_jm(kegg_gsea_sig,"core_enrichment")